What do we want to estimate from observational datasets? Choosing appropriate statistical analysis methods based on the chemical management phase

The goals of observational dataset analysis vary with the management phase of environments threatened by anthropogenic chemicals. For example, identifying severely compromised sites is necessary to determine candidate sites in which to implement measures during early management phases. Among the most effective approaches is developing regression models with high predictive power for dependent variable values using the Akaike information criterion. However, this analytical approach may be theoretically inappropriate to obtain the necessary information in various chemical management phases, such as the intervention effect size of a chemical required in the late chemical management phase to evaluate the necessity of an effluent standard and its specific value. However, choosing appropriate statistical methods based on the data analysis objective in each chemical management phase has rarely been performed. This study provides an overview of the primary data analysis objectives in the early and late chemical management phases. For each objective, several suitable statistical analysis methods for observational datasets are detailed. In addition, the study presents examples of linear regression analysis procedures using an available dataset derived from field surveys conducted in Japanese rivers. Integr Environ Assess Manag 2022;00:1-9. (c) 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Automated summary

This study provides an overview of the primary data analysis objectives in the early and late chemical management phases, as well as suitable statistical analysis methods for observational datasets. Examples of linear regression analysis procedures using field survey data from Japanese rivers are presented.